Loan Data from Prosper

by - Shaurya Sinha

Preliminary Wrangling

This data set contains 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, and many others.

  • This data dictionary explains the variables in the data set.
  • You are not expected to explore all of the variables in the dataset! Focus your exploration on about 10-15 of them.

Importing libraries

In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

Loading Dataset

In [2]:
df = pd.read_csv("prosperLoanData.csv")
df.head()
Out[2]:
ListingKey ListingNumber ListingCreationDate CreditGrade Term LoanStatus ClosedDate BorrowerAPR BorrowerRate LenderYield ... LP_ServiceFees LP_CollectionFees LP_GrossPrincipalLoss LP_NetPrincipalLoss LP_NonPrincipalRecoverypayments PercentFunded Recommendations InvestmentFromFriendsCount InvestmentFromFriendsAmount Investors
0 1021339766868145413AB3B 193129 2007-08-26 19:09:29.263000000 C 36 Completed 2009-08-14 00:00:00 0.16516 0.1580 0.1380 ... -133.18 0.0 0.0 0.0 0.0 1.0 0 0 0.0 258
1 10273602499503308B223C1 1209647 2014-02-27 08:28:07.900000000 NaN 36 Current NaN 0.12016 0.0920 0.0820 ... 0.00 0.0 0.0 0.0 0.0 1.0 0 0 0.0 1
2 0EE9337825851032864889A 81716 2007-01-05 15:00:47.090000000 HR 36 Completed 2009-12-17 00:00:00 0.28269 0.2750 0.2400 ... -24.20 0.0 0.0 0.0 0.0 1.0 0 0 0.0 41
3 0EF5356002482715299901A 658116 2012-10-22 11:02:35.010000000 NaN 36 Current NaN 0.12528 0.0974 0.0874 ... -108.01 0.0 0.0 0.0 0.0 1.0 0 0 0.0 158
4 0F023589499656230C5E3E2 909464 2013-09-14 18:38:39.097000000 NaN 36 Current NaN 0.24614 0.2085 0.1985 ... -60.27 0.0 0.0 0.0 0.0 1.0 0 0 0.0 20

5 rows × 81 columns

In [3]:
# Exploring the columns
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 113937 entries, 0 to 113936
Data columns (total 81 columns):
 #   Column                               Non-Null Count   Dtype  
---  ------                               --------------   -----  
 0   ListingKey                           113937 non-null  object 
 1   ListingNumber                        113937 non-null  int64  
 2   ListingCreationDate                  113937 non-null  object 
 3   CreditGrade                          28953 non-null   object 
 4   Term                                 113937 non-null  int64  
 5   LoanStatus                           113937 non-null  object 
 6   ClosedDate                           55089 non-null   object 
 7   BorrowerAPR                          113912 non-null  float64
 8   BorrowerRate                         113937 non-null  float64
 9   LenderYield                          113937 non-null  float64
 10  EstimatedEffectiveYield              84853 non-null   float64
 11  EstimatedLoss                        84853 non-null   float64
 12  EstimatedReturn                      84853 non-null   float64
 13  ProsperRating (numeric)              84853 non-null   float64
 14  ProsperRating (Alpha)                84853 non-null   object 
 15  ProsperScore                         84853 non-null   float64
 16  ListingCategory (numeric)            113937 non-null  int64  
 17  BorrowerState                        108422 non-null  object 
 18  Occupation                           110349 non-null  object 
 19  EmploymentStatus                     111682 non-null  object 
 20  EmploymentStatusDuration             106312 non-null  float64
 21  IsBorrowerHomeowner                  113937 non-null  bool   
 22  CurrentlyInGroup                     113937 non-null  bool   
 23  GroupKey                             13341 non-null   object 
 24  DateCreditPulled                     113937 non-null  object 
 25  CreditScoreRangeLower                113346 non-null  float64
 26  CreditScoreRangeUpper                113346 non-null  float64
 27  FirstRecordedCreditLine              113240 non-null  object 
 28  CurrentCreditLines                   106333 non-null  float64
 29  OpenCreditLines                      106333 non-null  float64
 30  TotalCreditLinespast7years           113240 non-null  float64
 31  OpenRevolvingAccounts                113937 non-null  int64  
 32  OpenRevolvingMonthlyPayment          113937 non-null  float64
 33  InquiriesLast6Months                 113240 non-null  float64
 34  TotalInquiries                       112778 non-null  float64
 35  CurrentDelinquencies                 113240 non-null  float64
 36  AmountDelinquent                     106315 non-null  float64
 37  DelinquenciesLast7Years              112947 non-null  float64
 38  PublicRecordsLast10Years             113240 non-null  float64
 39  PublicRecordsLast12Months            106333 non-null  float64
 40  RevolvingCreditBalance               106333 non-null  float64
 41  BankcardUtilization                  106333 non-null  float64
 42  AvailableBankcardCredit              106393 non-null  float64
 43  TotalTrades                          106393 non-null  float64
 44  TradesNeverDelinquent (percentage)   106393 non-null  float64
 45  TradesOpenedLast6Months              106393 non-null  float64
 46  DebtToIncomeRatio                    105383 non-null  float64
 47  IncomeRange                          113937 non-null  object 
 48  IncomeVerifiable                     113937 non-null  bool   
 49  StatedMonthlyIncome                  113937 non-null  float64
 50  LoanKey                              113937 non-null  object 
 51  TotalProsperLoans                    22085 non-null   float64
 52  TotalProsperPaymentsBilled           22085 non-null   float64
 53  OnTimeProsperPayments                22085 non-null   float64
 54  ProsperPaymentsLessThanOneMonthLate  22085 non-null   float64
 55  ProsperPaymentsOneMonthPlusLate      22085 non-null   float64
 56  ProsperPrincipalBorrowed             22085 non-null   float64
 57  ProsperPrincipalOutstanding          22085 non-null   float64
 58  ScorexChangeAtTimeOfListing          18928 non-null   float64
 59  LoanCurrentDaysDelinquent            113937 non-null  int64  
 60  LoanFirstDefaultedCycleNumber        16952 non-null   float64
 61  LoanMonthsSinceOrigination           113937 non-null  int64  
 62  LoanNumber                           113937 non-null  int64  
 63  LoanOriginalAmount                   113937 non-null  int64  
 64  LoanOriginationDate                  113937 non-null  object 
 65  LoanOriginationQuarter               113937 non-null  object 
 66  MemberKey                            113937 non-null  object 
 67  MonthlyLoanPayment                   113937 non-null  float64
 68  LP_CustomerPayments                  113937 non-null  float64
 69  LP_CustomerPrincipalPayments         113937 non-null  float64
 70  LP_InterestandFees                   113937 non-null  float64
 71  LP_ServiceFees                       113937 non-null  float64
 72  LP_CollectionFees                    113937 non-null  float64
 73  LP_GrossPrincipalLoss                113937 non-null  float64
 74  LP_NetPrincipalLoss                  113937 non-null  float64
 75  LP_NonPrincipalRecoverypayments      113937 non-null  float64
 76  PercentFunded                        113937 non-null  float64
 77  Recommendations                      113937 non-null  int64  
 78  InvestmentFromFriendsCount           113937 non-null  int64  
 79  InvestmentFromFriendsAmount          113937 non-null  float64
 80  Investors                            113937 non-null  int64  
dtypes: bool(3), float64(50), int64(11), object(17)
memory usage: 68.1+ MB

What is the structure ?

81 columns and 113937 rows

What is/are the main feature(s) of interest in your dataset?

the loan amounts and loan amount > 10000 the most convinient way to get it should have which factors.

What features in the dataset do you think will help support your investigation into your feature(s) of interest?

Credit Score/grade , Prosper Score , borrower Rate , Occupation , etc

What are the diffrent LoanStatus and what is the distribution of them ?

In [4]:
# A Count plot of Loan Status in genral loans
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = sns.cubehelix_palette(12  , reverse=True)
sns.countplot(x=df['LoanStatus'], ax=ax , order=df['LoanStatus'].value_counts().index , palette=cmap)
plt.xticks(rotation=15)
plt.title("Diffrent Loan Status Counts (in gen)")
for i, v in df['LoanStatus'].value_counts().reset_index().iterrows():
    ax.text(i, v.LoanStatus + 0.2 , v.LoanStatus, color='red')
In [5]:
# A Count plot of Loan Status in elite loans
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = sns.cubehelix_palette(12  , reverse=True)
sns.countplot(x=df.query("LoanOriginalAmount > 10000")['LoanStatus'], ax=ax , order=df.query("LoanOriginalAmount > 10000")['LoanStatus'].value_counts().index , palette=cmap)
plt.xticks(rotation=15)
plt.title("Diffrent Loan Status Counts (in elite)")
for i, v in df.query("LoanOriginalAmount > 10000")['LoanStatus'].value_counts().reset_index().iterrows():
    ax.text(i, v.LoanStatus + 0.2 , v.LoanStatus, color='red')
  • Forr all the loans in genral Most of the people have completed their payment fo the loan however a significant proportion still is defaulted or is past due by 1-120 days
  • However in the case of Elite Loans(loans>10000) the completion rate is very low as compared to all the loans in genral

Which Type/Category of loans are mostly given out ?

In [6]:
# Listing category numeric top 5 counts
df = df.rename(columns={"ListingCategory (numeric)":"ListingCategory"})
df['ListingCategory'].value_counts().head()
Out[6]:
1    58308
0    16965
7    10494
2     7433
3     7189
Name: ListingCategory, dtype: int64
In [7]:
# A Count plot of Listing Category for genral loans
new = ["Not Available" , "Debt Consolidation", "Home Improvement" , "Business" , "Personal Loan" , "Student Use" , "Auto" , "Other" , "Baby&Adoption",
      "Boat" , "Cosmetic Procedure" , "Engagement Ring" , "Green Loans" , "Household Expenses" , "Large Purchases" , "Medical/Dental" , "Motorcycle" ,
      "RV" , "Taxes" , "Vacation" , "Wedding Loans"]
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = sns.cubehelix_palette(20 , reverse=True)
sns.countplot(x=df['ListingCategory'], ax=ax , order=df['ListingCategory'].value_counts().index , palette=cmap)
labels = [item.get_text() for item in ax.get_xticklabels()]
for i in range(21):
    labels[i] = new[int(labels[i])]

ax.set_xticklabels(labels)
plt.xticks(rotation=25)
plt.title("Loans given out according to Category (in gen)")
for i, v in df['ListingCategory'].value_counts().reset_index().iterrows():
    ax.text(i, v.ListingCategory + 0.2 , v.ListingCategory, color='red');
In [8]:
# A Count plot of Listing Category for Elite Loans
new = ["Not Available" , "Debt Consolidation", "Home Improvement" , "Business" , "Personal Loan" , "Student Use" , "Auto" , "Other" , "Baby&Adoption",
      "Boat" , "Cosmetic Procedure" , "Engagement Ring" , "Green Loans" , "Household Expenses" , "Large Purchases" , "Medical/Dental" , "Motorcycle" ,
      "RV" , "Taxes" , "Vacation" , "Wedding Loans"]
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = sns.cubehelix_palette(20 , reverse=True)
sns.countplot(x=df.query("LoanOriginalAmount > 10000")['ListingCategory'], ax=ax , order=df.query("LoanOriginalAmount > 10000")['ListingCategory'].value_counts().index , palette=cmap)
labels = [item.get_text() for item in ax.get_xticklabels()]
for i in range(21):
    labels[i] = new[int(labels[i])]

ax.set_xticklabels(labels)
plt.xticks(rotation=25)
plt.title("Loans given out according to Category (in elite)")
for i, v in df.query("LoanOriginalAmount > 10000")['ListingCategory'].value_counts().reset_index().iterrows():
    ax.text(i, v.ListingCategory + 0.2 , v.ListingCategory, color='red');
  • we see that most of the loans given out were to cover off other debts i.e. Debt Consolidation in both the cases Normal loans in. genal and Elite Loans(loan>10000)
  • Least loans were given to boats , green Loans and RV in case of genral Loans
  • While in case of Elite Loans the least no of loans were given to Student Use , Rv and Cosmetic Procedure

Which occupation Takes most no of loans ?

In [9]:
# Diffrent Occupations available
df["Occupation"].value_counts().iloc[0:20]
Out[9]:
Other                                28617
Professional                         13628
Computer Programmer                   4478
Executive                             4311
Teacher                               3759
Administrative Assistant              3688
Analyst                               3602
Sales - Commission                    3446
Accountant/CPA                        3233
Clerical                              3164
Sales - Retail                        2797
Skilled Labor                         2746
Retail Management                     2602
Nurse (RN)                            2489
Construction                          1790
Truck Driver                          1675
Laborer                               1595
Police Officer/Correction Officer     1578
Civil Service                         1457
Engineer - Mechanical                 1406
Name: Occupation, dtype: int64
In [10]:
# A Count plot of Diffent occupations in genral
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = sns.cubehelix_palette(20  , reverse=True)
sns.countplot(x=df["Occupation"], ax=ax , order=df["Occupation"].value_counts().iloc[2:20].index , palette=cmap)
plt.xticks(rotation=45)
plt.title("Count of loans given out by Ocuupation (in gen)")
for i, v in df["Occupation"].value_counts().iloc[2:20].reset_index().iterrows():
    ax.text(i, v.Occupation + 0.2 , v.Occupation, color='red');
In [11]:
# A Count plot of Diffent occupations in elite loans
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = sns.cubehelix_palette(20  , reverse=True)
sns.countplot(x=df.query("LoanOriginalAmount > 10000")["Occupation"], ax=ax , order=df.query("LoanOriginalAmount > 10000")["Occupation"].value_counts().iloc[2:20].index , palette=cmap)
plt.xticks(rotation=45)
plt.title("Count of loans given out by Ocuupation (in elite)")
for i, v in df.query("LoanOriginalAmount > 10000")["Occupation"].value_counts().iloc[2:20].reset_index().iterrows():
    ax.text(i, v.Occupation + 0.2 , v.Occupation, color='red');
  • On an Average most no of Loans were taken by Computer Programmers followed by Executive and then teacher
  • While in the case of Elite Loans (loans >10000) most loans were taken by Executive Followed by computer programmer and then analyst
  • On an Average least no of Loans were taken by Police Officer , civil Servise , Engineer mechanical
  • While in the case of Elite Loans (loans >10000) least loans were taken by truck driver , sales-retail , Engineer-Electrical

Which Credit Grade Takes most no of loans ?

In [12]:
# diffent Credit Grades
df["CreditGrade"].value_counts()
Out[12]:
C     5649
D     5153
B     4389
AA    3509
HR    3508
A     3315
E     3289
NC     141
Name: CreditGrade, dtype: int64
In [13]:
# A Count plot of Diffent Credit Grades in genral
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = sns.cubehelix_palette(9  , reverse=True)
sns.countplot(x=df["CreditGrade"], ax=ax , order=df["CreditGrade"].value_counts().index , palette=cmap)
plt.title("Count of loans given out by Credit Grade (in gen)")
for i, v in df["CreditGrade"].value_counts().reset_index().iterrows():
    ax.text(i, v.CreditGrade + 0.2 , v.CreditGrade, color='red');
In [14]:
# A Count plot of Diffent Credit Grades in elite loans
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = sns.cubehelix_palette(9  , reverse=True)
sns.countplot(x=df.query("LoanOriginalAmount > 10000")["CreditGrade"], ax=ax , order=df.query("LoanOriginalAmount > 10000")["CreditGrade"].value_counts().index , palette=cmap)
plt.title("Count of loans given out by Credit Grade (in elite)")
for i, v in df.query("LoanOriginalAmount > 10000")["CreditGrade"].value_counts().reset_index().iterrows():
    ax.text(i, v.CreditGrade + 0.2 , v.CreditGrade, color='red');
  • in case of all the loans taken into acount people with credit grade of C , D , B were gven more preference while people with credit grade of NC were less
  • while in case of Elite loans(loans>10000) loans people with credit grade of B , AA , A were gven more preference while people with credit grade of HR were less

Which Sector Takes most no of loans ?

In [15]:
# A Count plot of Diffent Employment Status in genral
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = sns.cubehelix_palette(9  , reverse=True)
sns.countplot(x=df["EmploymentStatus"], ax=ax , order=df["EmploymentStatus"].value_counts().index , palette=cmap)
plt.title("Count of loans given out by Employment Status (in gen)")
for i, v in df["EmploymentStatus"].value_counts().reset_index().iterrows():
    ax.text(i, v.EmploymentStatus + 0.2 , v.EmploymentStatus, color='red');
In [16]:
# A Count plot of Diffent Employment Status in elite loans
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = sns.cubehelix_palette(9  , reverse=True)
sns.countplot(x=df.query("LoanOriginalAmount > 10000")["EmploymentStatus"], ax=ax , order=df.query("LoanOriginalAmount > 10000")["EmploymentStatus"].value_counts().index , palette=cmap)
plt.title("Count of loans given out by Employment Status (in elite)")
for i, v in df.query("LoanOriginalAmount > 10000")["EmploymentStatus"].value_counts().reset_index().iterrows():
    ax.text(i, v.EmploymentStatus + 0.2 , v.EmploymentStatus, color='red');
  • People who were employed were given more preference both in normal and Elite loans(loans>10000) while they also are the once to ask for loans the most

Count of people who recieved loan of diffrent amounts.

In [17]:
# ahistogram depecting the relationship between diffrent ranges of loan amount and no of people who recieved it
fig, ax = plt.subplots(figsize=(20 , 10))
bins = np.arange(1000 , df['LoanOriginalAmount'].max()+1000 , 3000)
plt.hist(df['LoanOriginalAmount'] ,bins=bins , ec='blue' , alpha=0.6)
plt.xticks(bins , rotation=30)
plt.xlabel("loan amount")
plt.ylabel("no of people who recieved loan")
plt.title("Count of loans given out by Total Loan Amount");
  • the histogram is left skewed which tells us that most of the loans issued was between 1000-7000 range

Count of people with diffrent Prosper Score

In [18]:
# a histogram depicting the relationship between the ranges of prosper Scores and no of people having prosper
# score between that prosper score in genral loans
fig, ax = plt.subplots(figsize=(20 , 10))
bins = np.arange(df['ProsperScore'].min() , df['ProsperScore'].max()+1 , 1)
plt.hist(df['ProsperScore'] ,bins=bins , ec='blue' , alpha=0.6)
plt.xticks(bins , rotation=30)
plt.xlabel("Prosper Scores")
plt.ylabel("no of people with that Prosper Score")
plt.title("Count of peoples with given Prosper Score (in gen)");
In [19]:
# a histogram depicting the relationship between the ranges of prosper Scores and no of people having prosper
# score between that prosper score in elite loans
fig, ax = plt.subplots(figsize=(20 , 10))
bins = np.arange(df.query("LoanOriginalAmount > 10000")['ProsperScore'].min() , df.query("LoanOriginalAmount > 10000")['ProsperScore'].max()+1 , 1)
plt.hist(df.query("LoanOriginalAmount > 10000")['ProsperScore'] ,bins=bins , ec='green' ,color = 'green' , alpha=0.6)
plt.xticks(bins , rotation=30)
plt.xlabel("Prosper Scores")
plt.ylabel("no of people with that Prosper Score")
plt.title("Count of peoples with given Prosper Score (in elite)");
  • We see that most of the people considering all the loans have their Prosper Score between 4-9
  • while those who recieved Elite Loans(loan>10000) has their Prosper Score between 6-9

Relationship between BorrowerAPR BorrowerRate

In [20]:
# A scatterplot depicting a relationship between BorrowerAPR and BorrowerRate in case of genral loans
fig, ax = plt.subplots(figsize=(20 , 10))
sns.scatterplot(data=df , x="BorrowerAPR" , y="BorrowerRate")
plt.title("Borrower APR vs Borrower Rate (in gen)");
In [21]:
# A scatterplot depicting a relationship between BorrowerAPR and BorrowerRate in case of elite loans
fig, ax = plt.subplots(figsize=(20 , 10))
sns.scatterplot(data=df.query("LoanOriginalAmount > 10000") , x="BorrowerAPR" , y="BorrowerRate")
plt.title("Borrower APR vs Borrower Rate (in elite)");
  • from the above visualizations we get to know that BorrowerAPR BorrowerRate are linearly related

Relationship between Term and BorrowerRate

In [22]:
# A violinplot depicting a relationship between Term and BorrowerRate in case of genral loans
fig, ax = plt.subplots(figsize=(20 , 10))
sns.violinplot(data=df , x="Term" , y="BorrowerRate" , color='blue' );
plt.title("rerm vs BorrowerRate (in gen)");
In [23]:
# A violinplot depicting a relationship between Term and BorrowerRate in case of genral loans
fig, ax = plt.subplots(figsize=(20 , 10))
sns.violinplot(data=df.query("LoanOriginalAmount > 10000") , x="Term" , y="BorrowerRate" , color='green' );
plt.title("rerm vs BorrowerRate (in eite)");
  • In the case of normal ie all the loans in genral 36 monts term has usally the highest borrow rate
  • While in the case of elite loans(loan>10000) 60 months term had usually the highest borrow rate
  • we can see that with the increase of term usually the borrow rate can be expected to rise

Does being unemployed affect to get a larger loan

In [24]:
# A boxplot depicting a relationship between EmploymentStatus and LoanOriginalAmount in case of genral loans
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = [[0.9312692223325372, 0.8201921796082118, 0.7971480974663592],
        [0.9312692223325372, 0.8201921796082118, 0.7971480974663592],
        [0.9312692223325372, 0.8201921796082118, 0.7971480974663592],
        [0.9312692223325372, 0.8201921796082118, 0.7971480974663592],
        [0.9312692223325372, 0.8201921796082118, 0.7971480974663592],
        [0.1750865648952205, 0.11840023306916837, 0.24215989137836502],
        [0.9312692223325372, 0.8201921796082118, 0.7971480974663592],
        [0.9312692223325372, 0.8201921796082118, 0.7971480974663592],]
sns.boxplot(data=df , x="EmploymentStatus" , y="LoanOriginalAmount" , palette=cmap)
plt.xticks(rotation=25)
plt.title("Employment Status vs Loan Original Amount (in gen)");
In [25]:
# A boxplot depicting a relationship between EmploymentStatus and LoanOriginalAmount in case of elite loans
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = [[0.9312692223325372, 0.8201921796082118, 0.7971480974663592],
        [0.9312692223325372, 0.8201921796082118, 0.7971480974663592],
        [0.9312692223325372, 0.8201921796082118, 0.7971480974663592],
        [0.9312692223325372, 0.8201921796082118, 0.7971480974663592],
        [0.9312692223325372, 0.8201921796082118, 0.7971480974663592],
        [0.1750865648952205, 0.11840023306916837, 0.24215989137836502],
        [0.9312692223325372, 0.8201921796082118, 0.7971480974663592],
        [0.9312692223325372, 0.8201921796082118, 0.7971480974663592],]
sns.boxplot(data=df.query("LoanOriginalAmount > 10000") , x="EmploymentStatus" , y="LoanOriginalAmount" , palette=cmap)
plt.xticks(rotation=25)
plt.title("Employment Status vs Loan Original Amount (in elite)");

Does having good Prosper Score improves my chances of getting higher loan Amounts

In [26]:
# A boxplot depicting a relationship between Prosper Score and LoanOriginalAmount in case of genral loans
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = sns.cubehelix_palette(11 )
sns.boxplot(data=df , x="ProsperScore" , y="LoanOriginalAmount" , palette=cmap)
plt.xticks(rotation=25);
plt.title("prosper score vs loanAmount (in gen)");
In [27]:
# A boxplot depicting a relationship between Prosper Score and LoanOriginalAmount in case of elite loans
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = sns.cubehelix_palette(11 )
sns.boxplot(data=df.query("LoanOriginalAmount > 10000") , x="ProsperScore" , y="LoanOriginalAmount" , palette=cmap)
plt.xticks(rotation=25);
plt.title("prosper score vs loanAmount (in elite)");
  • From the above plots we can see that with the increse in prosper score loan amount also increased drastially

Does having good Credit Score/Grade affects the chances of getting larger loan amounts

In [28]:
# merging the CreditScoreRangeLower CreditScoreRangeUpper columns into one CreditScoreAvg average of both
col = df.loc[: , "CreditScoreRangeLower":"CreditScoreRangeUpper"]
df["CreditScoreAvg"] = col.mean(axis=1)
In [29]:
# Creatinhg a new column new_emp that cotains only 2 variables employed or not
df['new_emp'] = df["EmploymentStatus"]
In [30]:
df['new_emp'] = df['new_emp'].replace({"Self-employed": "Employed", "Full-time": "Employed", "Part-time": "Employed", "Retired": "Not employed"})
In [31]:
df["new_emp"].unique()
Out[31]:
array(['Employed', 'Not available', 'Other', nan, 'Not employed'],
      dtype=object)
In [32]:
# a stripplot that depicts the relationship between CreditScoreAvg and LoanOriginalAmount with new_emp 
# as the third variable in case of genral loans
fig, ax = plt.subplots(figsize=(20 , 10))
sns.stripplot(data=df , x="CreditScoreAvg" , y="LoanOriginalAmount" , hue = "new_emp");
plt.title("credit score vs loanAmount (in gen)");
In [33]:
# a stripplot that depicts the relationship between CreditScoreAvg and LoanOriginalAmount with new_emp 
# as the third variable in case of elite loans
fig, ax = plt.subplots(figsize=(20 , 10))
sns.stripplot(data=df.query("LoanOriginalAmount > 10000") , x="CreditScoreAvg" , y="LoanOriginalAmount" , hue = "new_emp" );

plt.title("credit score vs loanAmount wrt employ status (in elite)");
In [34]:
# a boxplot that depicts the relationship between Credit Grade and LoanOriginalAmount with new_emp 
# as the third variable in case of genral loans
fig, ax = plt.subplots(figsize=(20 , 10))
sns.boxplot(data=df.loc[df['new_emp'].isin(['Employed' , 'Not employed'])] , x="CreditGrade" , y="LoanOriginalAmount" , hue = "new_emp" );
plt.title("credit grade vs loanAmount wrt employed or not(in gen)");
In [35]:
# a boxplot that depicts the relationship between Credit Grade and LoanOriginalAmount with new_emp 
# as the third variable in case of genral loans
fig, ax = plt.subplots(figsize=(20 , 10))
sns.boxplot(data=df.query("LoanOriginalAmount > 10000").loc[df['new_emp'].isin(['Employed' , 'Not employed'])] , x="CreditGrade" , y="LoanOriginalAmount" , hue = "new_emp" );
plt.title("credit grade vs loanAmount wrt employed or not(in elite)");
In [36]:
# a stripplot that depicts the relationship between Credit Grade and LoanOriginalAmount with new_emp 
# as the third variable in case of genral loans
fig, ax = plt.subplots(figsize=(20 , 10))
sns.stripplot(data=df.loc[df['new_emp'].isin(['Employed' , 'Not employed'])] , x="CreditGrade" , y="LoanOriginalAmount" , hue = "new_emp" );
plt.title("credit grade vs loanAmount wrt employed or not(in gen)");
In [37]:
# a stripplot that depicts the relationship between Credit Grade and LoanOriginalAmount with new_emp 
# as the third variable in case of elite loans
fig, ax = plt.subplots(figsize=(20 , 10))
sns.stripplot(data=df.query("LoanOriginalAmount > 10000").loc[df['new_emp'].isin(['Employed' , 'Not employed'])] , x="CreditGrade" , y="LoanOriginalAmount" , hue = "new_emp" );
plt.title("credit grade vs loanAmount wrt employed or not(in elite)");
  • from the above visualization having credit score >729.5 and grade AA , A , B, D , C will ensure higher chances to get an Elite Loan(loan>10000)

Finding more relationships

In [38]:
# a heatmap of all the columns with |correlation| > 0.5 to fing relations
fig, ax = plt.subplots(figsize=(20 , 20))
df_corr = df.corr()
df_corr[np.abs(df_corr)<.50] = 0
mask = np.zeros_like(df_corr, dtype=np.bool)
mask[np.triu_indices_from(mask)] = True
sns.heatmap(df_corr , mask=mask , cmap='coolwarm' , linewidths=1)
plt.xlabel("columns")
plt.ylabel("columns")
plt.title("|correlation| > 0.5");
In [39]:
# from the above matrix heatmap we can derive a smaller heatmap
plt.figure(figsize=(15,15))
sns.heatmap(df[['BorrowerAPR', 'BorrowerRate', 'LenderYield', 'EstimatedEffectiveYield','EstimatedLoss' , 'EstimatedReturn' , 'ProsperRating (numeric)' , 'ProsperScore', 'CreditGrade', 'CreditScoreAvg', 'Term','ListingCategory']].corr() , annot=True);
plt.xlabel("columns")
plt.ylabel("columns")
plt.title("correlations");
In [40]:
# exploring the relationship between CreditScoreAvg ProsperScore using heatmap
sns.heatmap(df[['CreditScoreAvg' , 'ProsperScore']].corr() , annot=True)
plt.title("credit score and Prosper Score");
In [41]:
# exploring the relationship between term and Creditscoreavg using boxplot
fig, ax = plt.subplots(figsize=(20 , 10))
sns.boxplot(x ='Term', y ='CreditScoreAvg', data = df,  
              );
plt.title("credit score and Term");
In [42]:
# exploring the relationship between CreditScoreAvg and BorrowerAPR using stripplot
fig, ax = plt.subplots(figsize=(20 , 10))
sns.stripplot(data=df , x="CreditScoreAvg" , y="BorrowerAPR" , jitter = True, dodge = True);
plt.title("credit score and BorrowerAPR");
In [43]:
# exploring the relationship between ProsperRating (numeric) and BorrowerAPR using stripplot
fig, ax = plt.subplots(figsize=(20 , 10))
sns.stripplot(x ='ProsperRating (numeric)', y ='BorrowerAPR', data = df,  
              jitter = True, dodge = True);
plt.title("Prosper Rating and BorrowerAPR");
In [44]:
# Plotting Correlationds using scattermatrix 
df_scatter = df[['BorrowerAPR', 'BorrowerRate', 'LenderYield', 'EstimatedEffectiveYield','EstimatedLoss' , 'EstimatedReturn' , 'ProsperRating (numeric)' , 'ProsperScore', 'CreditGrade', 'CreditScoreAvg', 'Term','ListingCategory']]
sns.pairplot(df_scatter)
plt.xlabel("columns")
plt.ylabel("columns")
plt.title("correlations");

From the above visualizations we get to know

  • people with higher credit score usually have lower borrowAPR and vice versa
  • people with Lower Prosper Rating usually have Higher borrowAPR and vice versa
  • Lender Yield is directly proportional to borrowAPR , Borrow Rate and Estimated Effective Yield
  • Similarly many other findings are seen from the above scatter matrix

Finding Relationship for diffrent Loan Status

In [45]:
# exploring the relationship between BorrowerRate and LoanStatus using boxplot in genral loans
fig, ax = plt.subplots(figsize=(20 , 10))
sns.boxplot(x ='BorrowerRate', y ='LoanStatus', data = df)
plt.title("Loan STates and Borrower Rate (in gen)");
In [46]:
# exploring the relationship between BorrowerRate and LoanStatus using boxplot in elite loan
fig, ax = plt.subplots(figsize=(20 , 10))
sns.boxplot(x ='BorrowerRate', y ='LoanStatus', data = df.query("LoanOriginalAmount > 10000"))
plt.title("Loan STates and Borrower Rate (in elite)");
In [47]:
# exploring the relationship between ProsperRating and LoanStatus using heatmap in gen loan
fig, ax = plt.subplots(figsize=(20 , 10))
df_heatmap = df.pivot_table(index='LoanStatus',
                                          columns='ProsperRating (numeric)',
                                          values='ListingKey',
                           aggfunc=lambda x: len(x.unique()))
sns.heatmap(df_heatmap , annot=True)
plt.title("Loan STates and ProsperRating (in gen)")
Out[47]:
Text(0.5, 1, 'Loan STates and ProsperRating (in gen)')
In [48]:
# exploring the relationship between ProsperRating and ListingKey using heatmap in elite loan
fig, ax = plt.subplots(figsize=(20 , 10))
df_heatmap = df.query("LoanOriginalAmount > 10000").pivot_table(index='LoanStatus',
                                          columns='ProsperRating (numeric)',
                                          values='ListingKey',
                           aggfunc=lambda x: len(x.unique()))
sns.heatmap(df_heatmap , annot=True)
plt.title("Loan STates and ProsperRating (in elite)");
In [49]:
# exploring the relationship between LoanStatus and Term using heatmap in genral loan
fig, ax = plt.subplots(figsize=(20 , 10))
df_heatmap = df.pivot_table(index='LoanStatus',
                                          columns='Term',
                                          values='ListingKey',
                           aggfunc=lambda x: len(x.unique()))
sns.heatmap(df_heatmap , annot=True)
plt.title("Loan STates and Term (in gen)");
In [50]:
# exploring the relationship between LoanStatus and Term using heatmap in elite loan
fig, ax = plt.subplots(figsize=(20 , 10))
df_heatmap = df.query("LoanOriginalAmount > 10000").pivot_table(index='LoanStatus',
                                          columns='Term',
                                          values='ListingKey',
                           aggfunc=lambda x: len(x.unique()))
sns.heatmap(df_heatmap , annot=True)
plt.title("Loan STates and Term (in elite)");
In [51]:
# exploring the relationship between LoanStatus and CreditScoreAvg using heatmap in genral loan
fig, ax = plt.subplots(figsize=(20 , 10))
df_heatmap = df.pivot_table(index='LoanStatus',
                                          columns='CreditScoreAvg',
                                          values='ListingKey',
                           aggfunc=lambda x: len(x.unique()))
sns.heatmap(df_heatmap )
plt.title("Loan STates and CreditScoreAvg (in gen)");
In [52]:
# exploring the relationship between LoanStatus and CreditScoreAvg using heatmap in elite loan
fig, ax = plt.subplots(figsize=(20 , 10))
df_heatmap = df.query("LoanOriginalAmount > 10000").pivot_table(index='LoanStatus',
                                          columns='CreditScoreAvg',
                                          values='ListingKey',
                           aggfunc=lambda x: len(x.unique()))
sns.heatmap(df_heatmap )
plt.title("Loan STates and CreditScoreAvg (in gen)");

From the above visualizations we get to know-

  • Borrow rates for all the loan statuses were lower for elite oans(loan>10000) compared to the average of all the loans
  • elite loans tend to have terms 36 or 60 while on an average most loans have terms between 12-36
  • similarly for Prosper Ratings elite loans have prosper ratings > 4
  • elite loans have no cancellations and less delayed on an average

Loan Amounts recieved for diffrent Loan purposes

In [53]:
# exploring the relationship between ListingCategory and LoanOriginalAmount using barplot in genral loan
new = ["Not Available" , "Debt Consolidation", "Home Improvement" , "Business" , "Personal Loan" , "Student Use" , "Auto" , "Other" , "Baby&Adoption",
      "Boat" , "Cosmetic Procedure" , "Engagement Ring" , "Green Loans" , "Household Expenses" , "Large Purchases" , "Medical/Dental" , "Motorcycle" ,
      "RV" , "Taxes" , "Vacation" , "Wedding Loans"]
fig, ax = plt.subplots(figsize=(20 , 10))
cmap = sns.cubehelix_palette(21 , reverse=True)
sns.barplot(data=df , x="ListingCategory" , y="LoanOriginalAmount"  ,palette=cmap )
labels = [item.get_text() for item in ax.get_xticklabels()]
for i in range(21):
    labels[i] = new[int(labels[i])]
ax.set_xticklabels(labels)
plt.xticks(rotation=25);
plt.title("Loan Categories and Loan amount");
  • on an average the Loan amount for debt consolidation was th highest and lowest for student use

Observations:

  • Forr all the loans in genral Most of the people have completed their payment fo the loan however a significant proportion still is defaulted or is past due by 1-120 days
  • However in the case of Elite Loans(loans>10000) the completion rate is very low as compared to all the loans in genral
  • we see that most of the loans given out were to cover off other debts i.e. Debt Consolidation in both the cases Normal loans in. genal and Elite Loans(loan>10000)
  • Least loans were given to boats , green Loans and RV in case of genral Loans
  • While in case of Elite Loans the least no of loans were given to Student Use , Rv and Cosmetic Procedure
  • On an Average most no of Loans were taken by Computer Programmers followed by Executive and then teacher
  • While in the case of Elite Loans (loans >10000) most loans were taken by Executive Followed by computer programmer and then analyst
  • On an Average least no of Loans were taken by Police Officer , civil Servise , Engineer mechanical
  • While in the case of Elite Loans (loans >10000) least loans were taken by truck driver , sales-retail , Engineer-Electrical
  • in case of all the loans taken into acount people with credit grade of C , D , B were gven more preference while people with credit grade of NC were less
  • while in case of Elite loans(loans>10000) loans people with credit grade of B , AA , A were gven more preference while people with credit grade of HR were less
  • People who were employed were given more preference both in normal and Elite loans(loans>10000) while they also are the once to ask for loans the most
  • most of the loans issued was between 1000-7000 range
  • We see that most of the people considering all the loans have their Prosper Score between 4-9
  • while those who recieved Elite Loans(loan>10000) has their Prosper Score between 6-9
  • BorrowerAPR BorrowerRate are linearly related
  • In the case of normal ie all the loans in genral 36 monts term has usally the highest borrow rate
  • While in the case of elite loans(loan>10000) 60 months term had usually the highest borrow rate
  • with the increase of term usually the borrow rate can be expected to rise
  • with the increse in prosper score loan amount also increased drastially
  • having credit score >729.5 and grade AA , A , B, D , C will ensure higher chances to get an Elite Loan(loan>10000)
  • people with higher credit score usually have lower borrowAPR and vice versa
  • people with Lower Prosper Rating usually have Higher borrowAPR and vice versa
  • Lender Yield is directly proportional to borrowAPR , Borrow Rate and Estimated Effective Yield
  • Similarly many other findings are seen from the above scatter matrix
  • Borrow rates for all the loan statuses were lower for elite oans(loan>10000) compared to the average of all the loans
  • elite loans tend to have terms 36 or 60 while on an average most loans have terms between 12-36
  • similarly for Prosper Ratings elite loans have prosper ratings > 4
  • elite loans have no cancellations and less delayed on an average
  • on an average the Loan amount for debt consolidation was th highest and lowest for student use

Key Insights for Presentation

  • Having credit score >729.5 , grade AA , A , B, D , C and Prosper Score in between (6-9) and having a job of Executive Followed by computer programmer demanding a loan for Debt Consolidation will ensure higher chances to get an Elite Loan(loan>10000).
  • Lender Yield is directly proportional to borrowAPR , Borrow Rate and Estimated Effective Yield